Dexcom CGM Outperforms Abbott FreeStyle Libre FGM1

For those with impaired awareness of hypoglycaemia, Dexcom CGM is the superior choice.

In this head-to-head study in the United Kingdom (UK) comparing the impact on hypoglycaemia between the Dexcom G5® Mobile Continuous Glucose Monitoring (CGM) System and the Abbott FreeStyle Libre Flash Glucose Monitoring (FGM) System, the Dexcom G5 Mobile outperformed Libre FGM in reducing exposure to dangerously low hypoglycaemia levels. In a 24-hour period and at night, Libre FGM users increased time spent in hypoglycaemia, while Dexcom CGM users significantly reduced their time spent in hypoglycaemia, benefitting from the alerts and hypo alarm of the Dexcom G5 Mobile.

Study Objective & Methods

Objective: Evaluate the impact on time spent in hypoglycaemia in CGM and FGM users with impaired awareness of hypoglycaemia (IAH).

Research Design/Methods: • Randomized, controlled trial; 8-week treatment period • Adults with type 1 diabetes on multiple daily injections of insulin (n=39); split into CGM use (n=19) or FGM use (n=20) • Gold score* ≥4 (impaired hypo awareness or recent severe hypoglycaemic event)

Results

Overall Impact on HypoglycaemiaPrimary Outcome:Comparison of change in time spent in hypoglycaemia (<60 mg/dL or 3.3 mmol/L) of CGM users to FGM users:

• Dexcom G5 Mobile users reduced median time spent in hypoglycaemia from baseline by 43 minutes.†

• Libre FGM users increased median time spent in hypoglycaemia from baseline by 19 minutes.†

† Between-group p-value: .006; outcome calculated by converting median percentage point difference into minutes, based on 24-hour period.

*The Gold score measures hypoglycaemia awareness, based on a response to, “Do you know when your hypos are beginning”;7-point scale, with 1 representing “always aware” and 7 “never aware”. Scores ≥4 indicate IAH.

+–

Reduction of Overall Hypoglycaemia

+–

+–

Dexcom CGM users reduced time spent in hypoglycaemia, while Libre FGM users increased their exposure to hypoglycaemia.

REDUCEDOVERALL HYPOGLYCAEMIA

Dexcom CGM users decreased time in hypoglycaemia at night, while Libre FGM users experienced worsening hypoglycaemia.

REDUCEDNIGHTTIMEHYPOGLYCAEMIA

Dexcom CGM users saw a significant reduction in hypoglycaemia fear compared to Libre FGM users.

REDUCEDFEAR OF HYPOGLYCAEMIA

RESULTS

Reduction of Nighttime Hypoglycaemia

+–

+–

Reduced Fear of Hypoglycaemia

I HART CGM Study1

CGM First™

Across Multiple Thresholds, Dexcom CGM Use Decreased Time in Hypo While Libre FGM Use Increased Time

(8 w

ee

ks -

bas

elin

e)

<50 mg/dLor <2.8 mmol/L

<60 mg/dLor <3.3 mmol/L

<70 mg/dLor <3.9 mmol/L

Dexcom G5 Mobile CGM

20

0

-20

-40

40

-60

60

Abbott Freestyle Libre FGM

Med

ian

∆ M

ins

1

Research: Treatment

A randomized controlled pilot study of continuous

glucose monitoring and flash glucose monitoring in

people with Type 1 diabetes and impaired awareness of

hypoglycaemia

M. Reddy, N. Jugnee, A. El Laboudi, E. Spanudakis, S. Anantharaja and N. Oliver

Division of Diabetes, Endocrinology and Metabolism, Faculty of Medicine, Imperial College, London, UK

Accepted 5 December 2017

Abstract

Aim Hypoglycaemia in Type 1 diabetes is associated with mortality and morbidity, especially where awareness of

hypoglycaemia is impaired. Clinical pathways for access to continuous glucose monitoring (CGM) and flash glucose

monitoring technologies are unclear. We assessed the impact of CGM and flash glucose monitoring in a high-risk group

of people with Type 1 diabetes.

Methods A randomized, non-masked parallel group study was undertaken. Adults with Type 1 diabetes using a

multiple-dose insulin-injection regimen with a Gold score of ≥ 4 or recent severe hypoglycaemia were recruited.

Following 2 weeks of blinded CGM, they were randomly assigned to CGM (Dexcom G5) or flash glucose monitoring

(Abbott Freestyle Libre) for 8 weeks. The primary outcome was the difference in time spent in hypoglycaemia (below

3.3 mmol/l) from baseline to endpoint with CGM versus flash glucose monitoring.

Results Some 40 participants were randomized to CGM (n = 20) or flash glucose monitoring (n = 20). The

participants (24 men, 16 women) had a median (IQR) age of 49.6 (37.5–63.5) years, duration of diabetes of 30.0 (21.0–36.5) years and HbA1c of 56 (48–63) mmol/mol [7.3 (6.5–7.8)%]. The baseline median percentage time < 3.3 mmol/l

was 4.5% in the CGM group and 6.7% in the flash glucose monitoring. At the end-point the percentage time

< 3.3 mmol/l was 2.4%, and 6.8% respectively (median between group difference �4.3%, P = 0.006). Time spent in

hypoglycaemia at all thresholds, and hypoglycaemia fear, were different between groups, favouring CGM.

Conclusion CGM more effectively reduces time spent in hypoglycaemia in people with Type 1 diabetes and impaired

awareness of hypoglycaemia compared with flash glucose monitoring. (Clinical Trial Registry No: NCT03028220)

Diabet. Med. 00, 00–00 (2017)

Introduction

Type 1 diabetes accounts for 10–15% of the worldwide

diabetes prevalence and its incidence is increasing worldwide

by 3–5% percent annually [1]. Achieving optimal glucose

control, as measured by HbA1c, reduces the risk of micro-

and macrovascular complications, but can be challenging for

people living with Type 1 diabetes due to hypoglycaemia

[2–4].

Hypoglycaemia is a metabolic complication of Type 1

diabetes and is one of the major barriers to optimizing

glucose self-management. People with Type 1 diabetes on

average have 1.8 self-treated incidences of hypoglycaemia

per week, and 0.2–3.2 episodes of severe hypoglycaemia,

defined as hypoglycaemia requiring the assistance of a third

party, annually [5,6]. Recurrent hypoglycaemia erodes

hypoglycaemia awareness and impaired awareness is seen

in around a quarter of people with Type 1 diabetes [7].

However, this may be an underestimate, with self-reported

severe hypoglycaemia rates affected by driving regulations

and other considerations [8].

Impaired awareness of hypoglycaemia increases risk of

severe hypoglycaemia six-fold. Hypoglycaemia is one of the

postulated causes of the ‘dead in bed’ syndrome, which is the

Correspondence to: Nick Oliver. E-mail: nick.oliver@imperial.ac.uk

This is an open access article under the terms of the Creative Commons

Attribution-NonCommercial License, which permits use, distribution and

reproduction in any medium, provided the original work is properly cited and

is not used for commercial purposes.

ª 2017 The Authors.Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK. 1

DIABETICMedicine

DOI: 10.1111/dme.13561

leading cause of death in people under 40 years of age with

Type 1 diabetes [9,10]. There is a three- to four-fold higher

mortality in people with diabetes who self-reported severe

hypoglycaemia in the preceding 5 years [11]. Hypoglycaemia

is, and remains, a significant burden for people with Type 1

diabetes and carries with it mortality and morbidity.

Continuous glucose monitoring (CGM) devices display an

estimate of blood glucose, along with the trends in glucose

changes, in real time. In addition, they provide alert and

alarm features for hypo- and hyperglycaemia, and for times

of rapid glucose change. Use of CGM is associated with a

reduction in HbA1c [12], and reduced exposure to, and risk

of hypoglycaemia [13] in people using insulin pump and

multiple-dose injection regimens [14]. The impact on glucose

and hypoglycaemia outcomes has additionally been con-

firmed in people with Type 1 diabetes and impaired

hypoglycaemia awareness [15]. Flash glucose monitoring

does not provide real-time data with alerts and alarms, but

allows users to retrospectively review the preceding 8 h of

continuous glucose data, along with a contemporary esti-

mated blood glucose value and trend line. The glucose data

are made available when the user chooses to swipe the reader

over the sensor. In one study of people with Type 1 diabetes,

flash glucose monitoring was associated with a reduction in

time spent in hypoglycaemia in people with Type 1 diabetes

and an HbA1c close to target [16].

International guidance supports the use of CGM for

people with Type 1 diabetes [17], especially those at high

risk of hypoglycaemia [18]. However, the role of flash

glucose monitoring in the self-management of Type 1

diabetes is less clear, especially for people with impaired

awareness of hypoglycaemia, or at high risk of severe

hypoglycaemia, and despite uptake of flash glucose moni-

toring led by people with diabetes, evidence-based clinical

pathways to optimize access to the appropriate monitoring

technologies are not available.

This study aims to assess the impact of CGM and flash

glucose monitoring on hypoglycaemia in people with Type 1

diabetes and impaired awareness of hypoglycaemia using a

multiple-dose insulin injection regimen.

Methods

Study design and participants

This randomized, non-masked parallel group study was

conducted at a single specialist site in the United Kingdom

(UK). Ethical approval was obtained from the National

Health Service (NHS) Research Ethics Committee. Partici-

pants aged ≥ 18 years with Type 1 diabetes for > 3 years

were recruited. In addition, participants had experienced a

severe hypoglycaemic event in the last 12 months requiring

third-party assistance or had a Gold score of ≥ 4. Those with

severe hypoglycaemia and a Gold score of < 4 may not have

impaired hypoglycaemia awareness; however, severe hypo-

glycaemia is associated with impaired awareness of hypogly-

caemia and they have therefore been included in this high-risk

study population. They had been using an intensified multi-

ple-dose insulin injection regimen for over 6 months and a

diagnosis of Type 1 diabetes was confirmed based on clinical

features and a fasting c-peptide < 200 pmol/l. All participants

had received Type 1 diabetes education, including the

principles of flexible insulin therapy, either as a group or in

a one-to-one environment from a specialist educator. Partic-

ipants were excluded if they had used CGM or flash glucose

monitoring within the last 6 months (except short periods of

diagnostic blinded use under clinic supervision), used regular

paracetamol, were pregnant or planning pregnancy, breast-

feeding, enrolled in other clinical trials, had active malig-

nancy or were under investigation for malignancy, had severe

visual impairment, or reduced manual dexterity. All partic-

ipants gave written informed consent.

Procedures

At study enrolment, participants gave a full medical and

medication history, and underwent a physical examination

and electrocardiogram. Fasting venous blood tests were

taken to assess HbA1c, plasma glucose, urea and electrolytes,

cortisol, and serum c-peptide. Women of childbearing age

had a urine pregnancy test. The Gold Score, Hypoglycaemia

Fear Score II (HFS-II), and Problem Areas in Diabetes (PAID)

questionnaires were completed. The Gold score is given by

subjective rating on a scale from 1 (always) to 7 (never) in

response to the question ‘Do you know when your hypos are

commencing?’. Participants meeting the inclusion criteria

had a brief Type 1 diabetes education refresher. Participants

then commenced a two-week run-in phase using the Dexcom

What’s new?

• This is the first head-to-head glucose monitoring study

comparing continuous glucose monitoring (CGM) and

flash glucose monitoring. This study addresses the

highest risk group with problematic and severe hypo-

glycaemia.

• CGM has a greater beneficial impact on hypoglycaemia

outcomes than flash glucose monitoring for people at

high risk of hypoglycaemia.

• The data contribute to the existing CGM literature and

are the first for flash glucose monitoring in a high-risk

group, expanding the evidence base.

• The results are clinically relevant and support a role for

CGM in the clinical pathway in people with severe

hypoglycaemia or impaired awareness of hypogly-

caemia.

2ª 2017 The Authors.

Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.

DIABETICMedicine Comparison of continuous and flash glucose monitoring in Type 1 diabetes � M. Reddy et al.

(San Diego, CA, USA) G4 sensor with a blinded receiver

running the advanced ‘505’ algorithm which stores glucose

data, but does not make it available to the participant. The

sensor was calibrated to capillary blood glucose values a

minimum of twice daily. From these blinded CGM data

baseline glucose metrics were calculated.

Participants were randomly assigned to CGM (Dexcom

G5) or flash glucose monitoring (Abbott Freestyle Libre) in a

1 :1 ratio using an online randomization tool (www.sealede

nvelope.com). Randomization was stratified by HbA1c

(< 58 mmol/mol and ≥ 58 mmol/mol). The treatment period

was 8 weeks.

Participants then received standardized CGM education for

the CGM (Dexcom G5) or flash glucose monitoring (Abbott

Freestyle Libre) devices, including the use of the absolute

value, rate of change arrow and glucose trend line. Both the

CGM and flash glucose monitoring systems were used non-

adjunctively (without capillary blood glucose verification

before making a treatment decision), in accordance with

product licences but participants were instructed to test their

capillary blood glucose if symptoms of hypo- or hypergly-

caemia occur, in case of sensor failure or if the sensor glucose

is out of the device’s range. Participants used the sensors with

the accompanying receivers and changed the sensor according

to the license (every 7 days for Dexcom G5, every 14 days for

Freestyle Libre) or sooner in the event of sensor failure. There

was a telephone visit 2 weeks after randomization focusing

on the function of the technology and any difficulties with

use. Participants attended the clinical research facility

4 weeks after randomization and data were downloaded

from their CGM and flash glucose monitoring devices using

the Diasend software. Eight weeks after randomization

participants attended the clinical research facility for a

venous blood test for HbA1c. They additionally completed

the Gold Score, HFS-II and PAID questionnaires, and data

were downloaded from their CGM and flash glucose mon-

itoring devices. Participants were provided with a contact

number for technical support, but insulin titration decisions

were made by the participant throughout the study. In the

CGM arm of the study, low glucose alert settings were

standardized at 4.4 mmol/l for all participants at randomiza-

tion and could be reduced to 4 mmol/l at week 2 during the

telephone visit depending on participant preference. High

glucose alerts were not protocolized.

Outcomes

The primary outcome was change in time spent in hypogly-

caemia (< 3.3 mmol/l) from baseline to endpoint with CGM

vs. flash glucose monitoring. Secondary outcomes were

percentage time spent in hypoglycaemia < 2.8, 3.5 and

3.9 mmol/l, percentage time in euglycaemia (3.9–7.8 mmol/l),

percentage time spent in target (3.9–10 mmol/l), percentage

time spent inhyperglycaemia> 7.8,> 10and> 15 mmol/l, low

blood glucose index (LBGI, a measure of hypoglycaemia risk

derived from continuous glucose data), severe hypoglycaemia

(requiring third-party assistance to treat), hypoglycaemia risk,

HbA1c,Gold Score, hypoglycaemia fear (HFS-II) and diabetes-

related emotional distress (PAID questionnaire). Baseline

continuous glucose data were taken from the first 14 days of

monitoring (the run-in phase) and endpoint outcomes calcu-

lated from the last 28 days in each treatment period.

Statistical analysis

In this pilot study we recruited n = 20 in each group (40

participants in total) which would demonstrate as significant

(P < 0.05) at 80% power a 0.92 standard deviation differ-

ence in mean change from baseline, in percentage time in

hypoglycaemia (< 3.3 mmol/l), between CGM and flash

glucose monitoring. Data were analysed using Stata v14

(StataCorp, College Station, TX, USA). Many variables were

not normally distributed and summary statistics are therefore

presented as median (IQR) and median change (95%

confidence interval). Outcomes at baseline and at 8 weeks

were analysed for CGM and for flash glucose monitoring

separately, and change from baseline to 8 weeks was

compared between the two interventions. The primary

outcome comparison was between CGM and flash glucose

monitoring in change in percentage time in hypoglycaemia

(< 3.3 mmol/l). Secondary outcome comparisons were con-

sidered as hypothesis-generating and informative. The Wil-

coxon rank sum test was used for comparing median changes

between groups. Analysis was by intention to treat. No data

monitoring committee was convened. The study is registered

at ClincialTrials.gov, number NCT03028220.

Results

We recruited 47 participants between 22 January 2016 and 7

December 2016. Seven participants were excluded and 40

were subsequently randomized to CGM (n = 20) or flash

glucose monitoring (n = 20) following the baseline run-in

period (Fig. 1, Table 1). Participants (24 men, 16 women)

had a median (IQR) age of 49.5 (37.5–63.5) years, duration

of diabetes of 30.0 (21.0–36.5) years, HbA1c of 56 (48–

63) mmol/mol (7.3 (6.5–7.8)%), Gold Score of 5 (4–5), and

episodes of self-reported hypoglycaemia per week of 3.0

(2.0–4.5). There were no significant differences in baseline

characteristics between the groups. Some 39 of the 40

participants included had a history of at least one episode of

severe hypoglycaemia in the past (6 months to 7 years ago).

Five participants were randomized with a history of severe

hypoglycaemia in the preceding year (and a Gold score of

< 4), all other participants had a Gold score of ≥ 4. Those

with a Gold score of < 4 all had a Gold score of 3 at baseline.

Two of these participants were randomized to the CGM

group and the remaining three to the flash glucose monitor-

ing group. All 40 randomized participants completed the

intervention period. For outcomes derived from CGM data

ª 2017 The Authors.Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK. 3

Research article DIABETICMedicine

n = 19 were analysed in the CGM group due to loss of the 8-

week CGM data for one participant resulting from uploading

error. A comparison of the glucose outcomes, derived from

the run-in blinded CGM data, between the CGM and flash

glucose monitoring groups was performed (using a non-

parametric test) and showed no statistical difference between

groups at baseline. None of the participants or their family/

friends downloaded the Dexcom Share app which allows

family members and friends to follow glucose trends and

alarms of the individual.

Median percentage time < 3.3 mmol/l fell from 4.5% to

2.4% in the CGM group and changed from to 6.7% to 6.8%

in the flash glucose monitoring group (Table 2). For the

primary outcome comparison, the median changes from

baseline to end-point for participants using CGM and flash

glucose monitoring were �3.0% and +1.3%< respectively

(P = 0.006). Accordingly, the median net effect of CGM

relative to flash glucose monitoring was a reduction of 4.3%

in percentage time < 3.3 mmol/l.

Within-group changes and significance levels for between

group differences for all CGM outcomes are reported in

Table 2. The same directionality of change and between-

group differences were found for hypoglycaemia outcomes

when overnight CGM data only (22:00 h to 07:00 h) were

analysed (Table 3). No significant between group differences

in change in time in target or in time spent above hypergly-

caemic thresholds were observed.

No episodes of severe hypoglycaemia were reported during

the 8-week intervention phase in either group.

At baseline 90% (18/20) of participants in the CGM group

and 85% (17/20) had a Gold score ≥ 4; at the 8-week end-

point this was reduced to 60% (12/20) in both groups,

indicating restored self-reported hypoglycaemia awareness in

a proportion of individuals. However, no significant differ-

ence was observed in overall Gold score from baseline to

end-point between the two groups (Table 4). No between-

group differences in HbA1c change were noted at 8 weeks.

The change in hypoglycaemia between group difference

was significant (P = 0.02; Table 4). This difference was

accounted for by changes in the worry sub-score of the HFS-

II (P = 0.02 for the between group difference). No within or

between group differences were noted in HFS-II behaviour

sub-score, and PAID scores.

Discussion

The results from this randomized parallel group pilot study

suggest that an 8-week intervention with CGM has a greater

benefit in reducing time in hypoglycaemia comparedwith flash

glucose monitoring in people with Type 1 diabetes and

impaired awareness of hypoglycaemia. Both CGM and flash

glucosemonitoring improvedHbA1c andpercentage time spent

in glucose target (3.9–7.8 and 3.9–10 mmol/l) over 8 weeks.

Finally, within- and between-group improvements in overall

Assessed for eligibility n = 47

Randomized n = 40

Allocated to CGMn = 20

Allocated to flash glucosemonitoring n = 20

Analysed n = 20* Analysed n = 20

7 participants excluded†

FIGURE 1 Participant recruitment. Results are expressed as median (IQR). *For outcomes derived from continuous glucose monitoring (CGM) data,

n = 19 were analysed in the G5 group due to loss of the 8-week CGM data for one participant (uploading error). †Reasons for participant exclusionwere: severe hypoglycaemia in run-in (1), failed to comply with visit schedule (3), dropped out due interference of study with exercise programme

(1), dropped out as they did not feel they could commit to the study (2).

Table 1 Baseline characteristics of study participants. Results are expressed as median (IQR)

CGM (n = 20) Flash glucose monitoring (n = 20) All participants (n = 40)

Gender (male : female) 12 : 8 12 : 8 24 : 16Age (years) 50.5 (45.0–64.5) 48.5 (34.0–63.0) 49.5 (37.5–63.5)Duration of diabetes (years) 30.0 (25.0–36.0) 28.0 (16.5–36.5) 30.0 (21.0–36.5)Gold score 5 (5–6) 5 (4–5) 5 (4–5)HbA1c (mmol/mol) 57 (49–62) 55 (48–65) 56 (48–63)HbA1c (%) 7.4 (6.6–7.8) 7.2 (6.5–8.1) 7.3 (6.5–7.8)Self-reported hypoglycaemia/week 3.0 (2.0–4.0) 2.5 (1.7–4.7) 3.0 (2.0–4.5)

CGM, continuous glucose monitoring.

4ª 2017 The Authors.

Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.

DIABETICMedicine Comparison of continuous and flash glucose monitoring in Type 1 diabetes � M. Reddy et al.

Table

2Medianpercentagetime(andIQ

R)spentwithin

variousglucose

ranges,atbaseline(w

eeks�2

to0)andendpoint(4–8

weeks),andmedianchange(and95%

confidence

interval)in

percentage

timeforcontinuousandflash

glucose

monitoring

Percentagetimewithin

defined

glucose

range

Continuousglucose

monitoring

(n=19)

Median(IQR)

Flash

glucose

monitoring(n

=20)

Median(IQR)

Medianchangefrom

baseline(95%

CI)

Baseline

(�2to

0weeks)

Endpoint

(4to

8weeks)

Baseline

(�2to

0weeks)

Endpoint

(4to

8weeks)

Continuousglucose

monitoring

Flash

glucose

monitoring

P-value

<2.8

mmol/l

2.3

(0.6–1

0.7)

0.9

(0.2–1

.8)

4.1

(2.5–5

.9)

3.8

(3.0–6

.4)

�1.2

(�4.3

to�0

.5)

1.3

(�1.0

to2.4)

0.003

<3.3

mmol/l

4.5

(1.9–1

4.1)

2.4

(1.0–5

.1)

6.7

(4.8–9

.5)

6.8

(4.8–1

1.7)

�3.0

(�5.0

to�0

.3)

1.3

(�1.4

to3.6)

0.006

<3.5

mmol/l

5.5

(3.1–1

5.7)

3.5

(1.8–6

.3)

8.0

(5.7–1

0.7)

8.2

(6.0–1

3.2)

�2.8

(�4.7

to�0

.3)

2.0

(�1.0

to4.7)

0.004

<3.9

mmol/l

8.8

(5.7–1

9.5)

6.2

(3.1–1

0.2)

11.9

(8.8–1

3.7)

11.0

(8.2–1

7.0)

�2.7

(�6.1

to�0

.1)

0.6

(�2.1

to5.4)

0.01

>7.8

mmol/l

48.8

(40.8–7

0.0)

49.0

(36.6–5

8.1)

50.3

(43.9–5

8.6)

47.1

(37.4–5

3.5)

�3.4

(�10.5

to1.4)

�5.9

(�15.0

to5.6)

0.57

>10mmol/l

33.3

(25.2–4

9.9)

26.7

(16.9–3

7.4)

35.0

(21.9–3

8.7)

28.0

(18.0–3

2.1)

�8.6

(�13.0

to�1

.1)

�7.0

(�16.9

to1.7)

0.71

>15mmol/l

10.0

(1.6–2

0.4)

4.2

(1.2–9

.7)

5.9

(2.7–9

.2)

2.6

(1.2–5

.1)

�4.9

(�8.6

to�0

.7)

�3.1

(�5.3

to�0

.4)

0.48

3.9–7

.8mmol/l

31.7

(24.1–4

3.8)

43.7

(34.7–5

2.3)

34.8

(30.2–4

4.1)

40.4

(34.7–4

5.3)

10.6

(3.3

to14.4)

5.9

(�2.4

to9.0)

0.15

3.9–1

0mmol/l

50.2

(40.8–6

6.5)

65.9

(53.5–7

4.8)

54.1

(47.5–6

4.5)

60.0

(54.5–6

7.8)

12.7

(7.2

to15.8)

5.3

(1.1

to11.7)

0.05

Table

3Medianpercentagetime(andIQ

R)spentwithin

variousglucose

ranges

overnight(22.00–0

7.00),atbaseline(w

eeks�2

to0)andendpoint(4–8

weeks),andthemedianchange(and95%

confidence

interval)in

percentagetimeforcontinuousandflash

glucose

monitoring

Percentagetimewithin

defined

glucose

range

Continuousglucose

monitoring

(n=19)

Median(IQR)

Flash

glucose

monitoring(n

=20)

Median(IQR)

Medianchangefrom

baseline(95%

CI)

Baseline

(�2to

0weeks)

Endpoint

(4–8

weeks)

Baseline

(�2to

0weeks)

Endpoint

(4–8

weeks)

Continuousglucose

monitoring

Flash

glucose

monitoring

P-value

<2.8

mmol/l

4.1

(0.5–1

3.1)

0.5

(0.0–2

.3)

5.1

(1.7–8

.2)

6.1

(3.6–1

0.8)

�2.7

(�6.1

to�0

.5)

1.2

(�1.5

to4.8)

0.001

<3.3

mmol/l

6.1

(2.9–1

7.3)

1.4

(0.4–5

.7)

8.3

(2.7–1

2.2)

8.8

(6.0–1

6.7)

�4.4

(�6.9

to0.0)

1.6

(�1.6

to6.0)

<0.001

<3.5

mmol/l

7.0

(3.9–1

8.7)

2.7

(0.6–7

.1)

9.5

(4.0–1

3.6)

10.2

(7.7–1

8.9)

�5.2

(�6.4

to0.0)

2.2

(�0.9

to6.6)

0.001

<3.9

mmol/l

9.6

(5.2–2

0.7)

5.5

(1.5–1

0.5)

13.0

(6.7–1

7.1)

12.6

(10.1– 2

2.0)

�4.8

(�9.5

to�0

.7)

3.1

(�2.7

to6.8)

0.004

>7.8

mmol/l

51.9

(36.9–6

8.9)

52.4

(35.5–6

3.3)

49.4

(34.4–6

4.6)

43.4

(30.5–6

0.7)

�1.9

(�11.1

to9.4)

�7.0

(�12.7

to3.4)

0.41

>10mmol/l

33.8

(13.5–5

3.1)

26.7

(11.2–4

4.6)

30.0

(16.6–4

4.9)

24.0

(13.5–3

2.5)

�4.4

(�15.4

to9.5)

�9.9

(�15.7

to�4

.3)

0.36

>15mmol/l

8.5

(1.0–1

3.8)

5.1

(0.5–8

.3)

5.4

(2.1–9

.8)

1.1

(0.7–4

.6)

�4.1

(�6.1

to0.0)

�2.9

(�6.1

to�1

.4)

0.70

3.9–7

.8mmol/l

31.8

(21.8–4

6.6)

42.8

(29.2–4

9.5)

37.6

(25.2–4

6.7)

41.4

(30.5–4

6.6)

13.0

(�4.1

to19.6)

4.1

(�1.0

to11.0)

0.16

3.9–1

0mmol/l

47.8

(39.2–6

5.9)

62.6

(51.7–7

2.7)

53.9

(42.3–6

7.5)

59.5

(52.1–6

4.2)

14.1

(�1.5

to23.7)

5.2

(0.7

to11.6)

0.20

ª 2017 The Authors.Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK. 5

Research article DIABETICMedicine

hypoglycaemia fear and the worry sub-scale of the hypogly-

caemia fear survey were seen with CGM. Awareness of

hypoglycaemia remained unchanged with both glucose mon-

itoring devices.

This is the first direct comparator study of continuous

glucose recording technologies assessing glucose outcomes

and aimed to provide supporting evidence for clinical

pathways implementing CGM and flash glucose monitoring

technologies. Around 25% of people with Type 1 diabetes

have impaired awareness of hypoglycaemia, and the associ-

ations with severe hypoglycaemia confer a burden of

mortality and morbidity. The data from this study suggest

that alerts and alarms are important for this high-risk group,

and that evidence-based clinical pathways must include a

measure of hypoglycaemia awareness prior to implementing

monitoring technologies where flash glucose monitoring may

not be first choice. A measure of hypoglycaemia awareness is

already included in the National Institute for Health and

Care Excellence (NICE) guideline for Type 1 diabetes in

adults [18]. In the IMPACT study [16] flash glucose

monitoring reduced time in hypoglycaemia, a finding we

have not replicated, but IMPACT excluded people with

impaired awareness of hypoglycaemia and recruited partic-

ipants with a lower mean HbA1c. These differences in the

population recruited may further indicate the importance of

selecting the appropriate technology for individuals with

Type 1 diabetes.

We did not see an improvement in self-reported awareness

of hypoglycaemia measured by Gold score with either CGM

or flash glucose monitoring. The lack of improvement in

Gold score with CGM is consistent with findings seen in the

IN CONTROL study [15] and in a retrospective audit [19].

The HypoCOMPaSS study showed that restoration of

hypoglycaemia awareness can be achieved, but that self-

monitoring capillary blood glucose and CGM have an

equivalent effect on impaired awareness of hypoglycaemia

[20]. However, the study designs and technologies imple-

mented differ, and further research is warranted to explore

the impacts of technology as an adjunct to education in

people with Type 1 diabetes and impaired awareness of

hypoglycaemia. A limitation to evaluating the Gold score

after the use of CGM or flash glucose monitoring is that it is

a subjective score and therefore does not distinguish whether

those who restored their hypoglycaemia awareness had true

recurrence of hypoglycaemia awareness from symptoms or

whether the glucose monitoring was providing ‘electronic

awareness’ by seeing the glucose trace falling or hearing the

alarms with CGM.

Our study is limited by small numbers and a short follow-

up period, but the population and study design are compa-

rable with previous reports in highly selected high-risk

groups. The baseline estimate of glucose data was derived

from blinded CGM in both groups, but the final glucose data

was derived from either CGM or flash glucose monitoring.

Therefore, a further limitation is the comparison betweenTable

4Median(andIQ

R)LBGI,Gold

score,HbA1c,HFS-totalscores,HFS-Behavioursubscores,HFS-W

orrysubscoresandPAID

scoresatbaselineandendpoint(at8weeks),andthemedianchange

(and95%

confidence

interval)forcontinuousandflash

glucose

monitoring.

Continuousglucose

monitoring

(n=19)

Median(IQR)

Flash

glucose

monitoring(n

=20)

Median(IQR)

Medianchangefrom

baseline

(95%

CI)

Baseline

Endpoint

(at8weeks)

Baseline

Endpoint

(at8weeks)

Continuousglucose

monitoring

Flash

glucose

monitoring

P-value

LBGI

7.0

(5.4–1

2.3)

5.3

(3.2–6

.3)

8.5

(5.9–9

.8)

9.1

(7.2–1

0.7)

�3.5

(�4.9

to�0

.9)

0.9

(�0.2

to3.0)

<0.001

Gold

score

5(5–6

)4.5

(3.0

-5.0)

5(4–5

)5.0

(3.5–6

.0)

0.0

(�1.0

to0.0)

0.0

(�0.8

to0.0)

0.23

HbA1c(m

mol/mol)

57(49–6

2)

54(45–6

1)

55(48–6

5)

51(48–5

9)

�1.5

(�8.6

to�1

.0)

�4.5

(�5.8

to0.0)

0.91

HbA1c(%

)7.4

(6.6–7

.8)

7.1

(6.3–7

.7)

7.2

(6.5–8

.1)

6.8

(6.5–7

.5)

�0.15(�

0.8

to�0

.05)

�0.35(�

0.6

to0.0)

HFS-totalscore

59.5

(37.0–7

8.0)

49.5

(28.0–7

4.0)

42.5

(32.0–5

6.5)

42.0

(28.5–6

5.5)

�6.5

(�10.8

to�2

.2)

�2.0

(�3.8

to2.8)

0.02

HFS-Behavioursubscore

21.0

(13.5–3

1.0)

20.0

(10.5–2

6.0)

17.5

(12.5–2

4.5)

15.0

(11.5–2

5.5)

�2.0

(�3.8

to�0

.1)

�0.5

(�3.0

to1.8)

0.36

HFS-W

orrysubscore

40.5

(24.0–5

2.5)

30.0

(17.5–4

4.0)

27.5

(18.0–3

4.5)

31.0

(15.5–4

6.0)

�4.5

(�7.8

to�0

.1)

0.5

(�3.0

to2.8)

0.02

PAID

score

31.0

(13.5–4

5.5)

28.5

(17.5–4

3.0)

19.0

(14.0–4

6.0)

22.0

(11.5–4

0.0)

�1.0

(�5.7

to4.8)

�1(�

5.0

to2.0)

0.82

6ª 2017 The Authors.

Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.

DIABETICMedicine Comparison of continuous and flash glucose monitoring in Type 1 diabetes � M. Reddy et al.

CGM and FGM data, where accuracy may not be equiva-

lent, so glucose outcomes may not be directly comparable.

This applies when evaluating the difference from baseline to

endpoint within the flash glucose monitoring group and

when comparing the two groups. However, the devices were

used in line with license and the relative published accura-

cies, expressed as a mean absolute difference, are between

11% and 13% for real-world use [21–24]. Another limita-

tion of our study is that stratification at randomization was

based on HbA1c alone and does not consider other factors

such as age, gender and diabetes duration. It is also

important to note that the reported times within range

reported are not independent (for example the percentage

time spent < 3.3mmol outcome includes percentage time

spent < 2.8 mmol/l). We recognize that the inclusion of

participants with severe hypoglycaemia and a Gold score of

< 4 makes the study population heterogeneous as those five

participants with a Gold score of < 4 may not have impaired

awareness of hypoglycaemia. This is a limitation, but these

participants belong to a high-risk population and were

randomized in an equal distribution (two in the CGM group

and five in the flash glucose monitoring group). The strength

of the study lies in its novelty and the clearly defined

homogeneous group of those at highest risk of challenging

hypoglycaemia.

A new consensus for reporting hypoglycaemia in studies as

< 3.0 mmol/l was recently recommended by The Interna-

tional Hypoglycaemia Study Group [25], but this was not the

case at the time of study design. The percentage time spent at

glucose < 3.0 mmol/l was therefore not a predetermined

study outcome in this study, but when analysed post hoc the

baseline vs. endpoint values were (3.1 vs. 1.5) and (4.7 vs.

5.0) in the CGM group and flash glucose monitoring group

respectively and there was a significant difference in median

change from baseline between groups (P = 0.004), suggest-

ing benefit with CGM.

The uptake of flash glucose monitoring has been striking

but, as yet, the technology has not been widely incorporated

into clinical guidelines where its role has been unclear. The

IMPACT study selected a specific group of people with

HbA1c values close to target and showed no change in HbA1c

but a reduction in time spent in hypoglycaemia compared

with self-monitoring of capillary blood glucose [16]. This

study adds to the IMPACT and DIAMOND studies and

suggests that CGM is preferable to flash glucose monitoring

for people with Type 1 diabetes using a multiple-dose

injection regimen with HbA1c values above target, and for

those with challenging hypoglycaemia.

One possible mechanism for the findings in our study is the

impact of alerts and alarms on behaviour and it is striking to

note that, alongside a reduction in exposure to hypogly-

caemia, we have demonstrated a reduction in hypoglycaemia

fear and worry. The changes to hypoglycaemia fear should

be confirmed in a larger study with a more heterogeneous

population.

Conclusion

In summary, our pilot data suggest that CGM has a

greater beneficial impact on hypoglycaemia outcomes

than flash glucose monitoring for people with impaired

hypoglycaemia awareness. Additionally, CGM has a

beneficial impact on hypoglycaemia fear, one of the

major barriers to optimal glucose control. The data

suggest that careful assessment of hypoglycaemia aware-

ness is critical to selecting the appropriate glucose

monitoring technology and that evidence-based clinical

pathways for monitoring should be different for people

with impaired awareness.

Funding sources

Dexcom funded the investigator-initiated study and provided

materials. The study was sponsored by Imperial College London.

Competing interests

NO has received honoraria for speaking and advisory board

participation from Abbott Diabetes, Dexcom, Medtronic

Diabetes and Roche Diabetes.

Acknowledgements

This paper presents independent research funded by Dexcom

and supported by the NIHR CRF and BRC at Imperial

College Healthcare NHS Trust. The views expressed are

those of the authors and not necessarily those of Dexcom, the

NHS, the NIHR or the Department of Health. The authors

would like to thank all of the study participants for their

valuable time.

Author contributions

MR, ES, NJ and SA ran the clinical study. MR performed the

statistical analysis. NO and MR wrote the first draft of the

report. NO designed the study, and AEL, MR and NO wrote

the protocol. NO is the guarantor of the study.

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RA, Smith SA. Increased mortality of patients with diabetes

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DIABETICMedicine Comparison of continuous and flash glucose monitoring in Type 1 diabetes � M. Reddy et al.

(8 w

ee

ks -

bas

elin

e)

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Dexcom CGM Use Reduced Time in Hypoglycaemia at Night; Increased Time Spent with Libre FGM UseDexcom CGM use reduced median time spent in hypoglycaemia (<60 mg/dL or 3.3 mmol/L) by 24 minutes from baseline while subjects using the Libre FGM increased exposure by 9 minutes.

• The audible alerts and alarm of the Dexcom G5 Mobile—which Libre FGM does not have—are especially helpful to patients with IAH at night, when they are most vulnerable to potentially dangerous hypoglycaemic incidents.1

Reduced Fear of Hypoglycaemia

• Participants in the Dexcom CGM group reported less fear of hypoglycaemia, a major barrier to good glucose control.

• No change in hypoglycaemia fear was reported in the Libre FGM group. (Between-group p-value: .02)

Dexcom CGM First™

Professional societies, including the ADA, AACE and the Endocrine Society now recognize CGM as a standard of care.2-4 Dexcom CGM use has been proven to both reduce A1C and decrease risk of hypoglycaemia regardless of insulin delivery method.5 When initiating or adjusting insulin regimens for your patients, Dexcom CGM provides real-time insights for better glycemic outcomes. Optimize your patients' diabetes treatment plans and recommend a Dexcom CGM System today.

For more information about adding Dexcom CGM to your patient’s diabetes treatment plan, visit dexcom.com/global

LBL015731 Rev001

I HART CGM Study1 Dexcom CGM Outperforms Abbott Freestyle Libre FGM (cont.)

+–

<50 mg/dLor <2.8 mmol/L

<60 mg/dLor <3.3 mmol/L

<70 mg/dLor <3.9 mmol/L

Dexcom CGM O�ers Added Protection at Night‡

Dexcom G5 Mobile CGM Abbott FreeStyle Libre FGM

Med

ian

∆ M

ins

‡ Nighttime hypoglycaemia measured from 22:00 - 07:00.

10

0

-10

-20

20

-30

30

©2017 Dexcom Inc. All rights reserved.

2